Machine Translation: Examples CS 188: Artificial Intelligence - - PDF document

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CS 188: Artificial Intelligence

Spring 2006

Lecture 28: Machine Translation 5/2/2006

Dan Klein – UC Berkeley

Machine Translation: Examples Levels of Transfer

Interlingua Semantic Structure Semantic Structure Syntactic Structure Syntactic Structure Word Structure Word Structure Source Text Target Text Semantic Composition Semantic Decomposition Semantic Analysis Semantic Generation Syntactic Analysis Syntactic Generation Morphological Analysis Morphological Generation Semantic Transfer Syntactic Transfer Direct

(Vauquois triangle)

General Approaches

Rule

• b

ased approaches

Expert system style rewrite systems Interlingua methods (analyze and generate) Lexicons come from humans or dictionaries Can be very fast, and can accumulate a lot of knowledge over time (e.g. Systran)

Statistical approaches

Noisy channel systems Lower-level transfer Lexicons discovered using parallel corpora Require little human declaration of knowledge

The Coding View

“One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: ‘This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.’ ”

Warren Weaver (1955:18, quoting a letter he wrote in 1947)

MT System Components

source P(e) e f decoder

• bserved

argmax P(e|f) = argmax P(f|e)P(e) e e e f best channel P(f|e)

Language Model Translation Model Finds an English translation which is both fluent and semantically faithful to the French source

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Language Models

Language Models

Any probabilistic model capable of assigning probabilities to sentences Usually n-gram models, but also PCFGs Exact same technology (and software) as in ASR Train on a huge collection of monolingual corpora (documents in the target language)

w1 w2 wn-1 STOP

START

Parallel Corpora

Parallel corpora (or bitexts)

Collection of source- target translation pairs Main resource for learning a translation model Either naturally occurring (e.g. parliamentary proceedings, news translation services) or commissioned

Building a Translation Model

Steps in building a simple statistical translation model

Match up words in training sentence pairs (word alignment) Learn a lexicon from these alignments Learn larger phrases

What is the anticipated cost

• f

collecting fees under the new proposal ? En vertu de les nouvelles propositions , quel est le coût prévu de perception de les droits ?

1-to-Many Alignments Many-to-Many Alignments The HMM Alignment Model

The HMM model (Vogel 96)

Re-estimate using the forward-backward algorithm Handling nulls requires some care

Note: alignments are not provided, but induced

• 2 -1 0 1 2 3

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Examples: Translation and Fertility

Phrases vs Word Models

il hoche la tête he is nodding

Extracting Phrases Basic Phrase-Based Model

[Koehn et al, 2003] Segmentation Translation Distortion

Decoding

Now we have a phrase table:

A huge list of translation phrases (e.g. 1M phrases) Each phrase has a probability P(f|e)

When we see a new input sentence:

Grow a translation left to right Extend translation using known phrases Also multiply by language model score

The Pharaoh Decoder

Probabilities at each step include LM and TM

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Some Output

Madame la présidente, votre présidence de cette institution a été marquante. Mrs Fontaine, your presidency of this institution has been outstanding. Madam President, president of this house has been discoveries. Madam President, your presidency of this institution has been impressive. Je vais maintenant m'exprimer brièvement en irlandais. I shall now speak briefly in Irish . I will now speak briefly in Ireland . I will now speak briefly in Irish . Nous trouvons en vous un président tel que nous le souhaitions. We think that you are the type of president that we want. We are in you a president as the wanted. We are in you a president as we the wanted.

Translations

Even human translators aren’t perfect:

In an Austrian ski hotel: Not to perambulate the corridors in the hours of repose in the boots of ascension. In a Copenhagen airline ticket office: We take your bags and send them in all directions. From a brochure of a car rental firm in Tokyo: When passenger of foot heave in sight, tootle the horn. Trumpet him melodiously at first, but if he still

• bstacles your passage then tootle him with vigor.

http://www.englishfirst.org/13166/funnytranslations.htm